Single Cell Transcriptomics identifies a WNT7A-FZD5 Signaling Axis that maintains Fallopian Tube Stem Cells in Patient-derived Organoids
Bibliographic record
Abstract
Despite its significance to reproduction, fertility, sexually transmitted infections and various pathologies, the fallopian tube (FT) is relatively understudied. Strong evidence points to the FT as the tissue-of-origin of high grade serous ovarian cancer (HGSOC), the most fatal gynaecological malignancy. HGSOC precursor lesions arise specifically in the distal FT (fimbria) which is reported to be enriched in stem-like cells. Investigation of the role of FT stem cells in health and disease has been hampered by a lack of characterization of FT stem cells and lack of models that recapitulate stem cell renewal and differentiation in vitro . Using optimized organoid culture conditions to address these limitations, we found that FT stem cell renewal is highly dependent on WNT/β-catenin signaling and engineered endogenous WNT/β-catenin signaling reporter organoids to biomark, isolate and characterize putative FT stem cells. Using functional approaches as well as bulk and single cell transcriptomic analyses, we show that an endogenous hormonally-regulated WNT7A-FZD5 signaling axis is critical for self-renewal of human FT stem cells, and that WNT/β-catenin pathway-activated FT cells form a distinct transcriptomic cluster of cells enriched in ECM remodelling and integrin signaling pathways. In addition, we find that the WNT7A-FZD5 signaling axis is dispensable for mouse oviduct regeneration. Overall, we provide a deep characterization of FT stem cells and their molecular requirements for self-renewal, paving the way for mechanistic work investigating the role of stem cells in FT health and disease. GRAPHICAL ABSTRACT
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".